"""

房价预测数据集 使用sklearn执行超参数搜索

"""

import matplotlib as mpl

import matplotlib.pyplot as plt

import numpy as np

import sklearn

import pandas as pd

import os

import sys

import tensorflow as tf

from tensorflow_core.python.keras.api._v2 import keras # 不能使用 python

from sklearn.preprocessing import StandardScaler

from sklearn.datasets import fetch_california_housing

from sklearn.model_selection import train_test_split, RandomizedSearchCV

from scipy.stats import reciprocal

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

assert tf.__version__.startswith('2.')

# 0.打印导入模块的版本

print(tf.__version__)

print(sys.version_info)

for module in mpl, np, sklearn, pd, tf, keras:

print("%s version:%s" % (module.__name__, module.__version__))

# 显示学习曲线

def plot_learning_curves(his):

pd.DataFrame(his.history).plot(figsize=(8, 5))

plt.grid(True)

plt.gca().set_ylim(0, 1)

plt.show()

# 1.加载数据集 california 房价

housing = fetch_california_housing()

print(housing.DESCR)

print(housing.data.shape)

print(housing.target.shape)

# 2.拆分数据集 训练集 验证集 测试集

x_train_all, x_test, y_train_all, y_test = train_test_split(

housing.data, housing.target, random_state=7)

x_train, x_valid, y_train, y_valid = train_test_split(

x_train_all, y_train_all, random_state=11)

print(x_train.shape, y_train.shape)

print(x_valid.shape, y_valid.shape)

print(x_test.shape, y_test.shape)

# 3.数据集归一化

scaler = StandardScaler()

x_train_scaled = scaler.fit_transform(x_train)

x_valid_scaled = scaler.fit_transform(x_valid)

x_test_scaled = scaler.fit_transform(x_test)

# 创建keras模型

def build_model(hidden_layers=1, # 中间层的参数

layer_size=30,

learning_rate=3e-3):

# 创建网络层

model = keras.models.Sequential()

model.add(keras.layers.Dense(layer_size, activation="relu",

input_shape=x_train.shape[1:]))

# 隐藏层设置

for _ in range(hidden_layers - 1):

model.add(keras.layers.Dense(layer_size,

activation="relu"))

model.add(keras.layers.Dense(1))

# 优化器学习率

optimizer = keras.optimizers.SGD(lr=learning_rate)

model.compile(loss="mse", optimizer=optimizer)

return model

def main():

# RandomizedSearchCV

# 1.转化为sklearn的model

sk_learn_model = keras.wrappers.scikit_learn.KerasRegressor(build_model)

callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]

history = sk_learn_model.fit(x_train_scaled, y_train, epochs=100,

validation_data=(x_valid_scaled, y_valid),

callbacks=callbacks)

# 2.定义超参数集合

# f(x) = 1/(x*log(b/a)) a <= x <= b

param_distribution = {

"hidden_layers": [1, 2, 3, 4],

"layer_size": np.arange(1, 100),

"learning_rate": reciprocal(1e-4, 1e-2),

}

# 3.执行超搜索参数

# cross_validation:训练集分成n份, n-1训练, 最后一份验证.

random_search_cv = RandomizedSearchCV(sk_learn_model, param_distribution,

n_iter=10,

cv=3,

n_jobs=1)

random_search_cv.fit(x_train_scaled, y_train, epochs=100,

validation_data=(x_valid_scaled, y_valid),

callbacks=callbacks)

# 4.显示超参数

print(random_search_cv.best_params_)

print(random_search_cv.best_score_)

print(random_search_cv.best_estimator_)

model = random_search_cv.best_estimator_.model

print(model.evaluate(x_test_scaled, y_test))

# 5.打印模型训练过程

plot_learning_curves(history)

if __name__ == '__main__':

main()